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        "\n# Species distribution modeling\n\n\nModeling species' geographic distributions is an important\nproblem in conservation biology. In this example we\nmodel the geographic distribution of two south american\nmammals given past observations and 14 environmental\nvariables. Since we have only positive examples (there are\nno unsuccessful observations), we cast this problem as a\ndensity estimation problem and use the `OneClassSVM` provided\nby the package `sklearn.svm` as our modeling tool.\nThe dataset is provided by Phillips et. al. (2006).\nIf available, the example uses\n`basemap <https://matplotlib.org/basemap/>`_\nto plot the coast lines and national boundaries of South America.\n\nThe two species are:\n\n - `\"Bradypus variegatus\"\n   <http://www.iucnredlist.org/details/3038/0>`_ ,\n   the Brown-throated Sloth.\n\n - `\"Microryzomys minutus\"\n   <http://www.iucnredlist.org/details/13408/0>`_ ,\n   also known as the Forest Small Rice Rat, a rodent that lives in Peru,\n   Colombia, Ecuador, Peru, and Venezuela.\n\nReferences\n----------\n\n * `\"Maximum entropy modeling of species geographic distributions\"\n   <http://rob.schapire.net/papers/ecolmod.pdf>`_\n   S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,\n   190:231-259, 2006.\n\n"
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        "# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com>\n#          Jake Vanderplas <vanderplas@astro.washington.edu>\n#\n# License: BSD 3 clause\n\nfrom __future__ import print_function\n\nfrom time import time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets.base import Bunch\nfrom sklearn.datasets import fetch_species_distributions\nfrom sklearn.datasets.species_distributions import construct_grids\nfrom sklearn import svm, metrics\n\n# if basemap is available, we'll use it.\n# otherwise, we'll improvise later...\ntry:\n    from mpl_toolkits.basemap import Basemap\n    basemap = True\nexcept ImportError:\n    basemap = False\n\nprint(__doc__)\n\n\ndef create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):\n    \"\"\"Create a bunch with information about a particular organism\n\n    This will use the test/train record arrays to extract the\n    data specific to the given species name.\n    \"\"\"\n    bunch = Bunch(name=' '.join(species_name.split(\"_\")[:2]))\n    species_name = species_name.encode('ascii')\n    points = dict(test=test, train=train)\n\n    for label, pts in points.items():\n        # choose points associated with the desired species\n        pts = pts[pts['species'] == species_name]\n        bunch['pts_%s' % label] = pts\n\n        # determine coverage values for each of the training & testing points\n        ix = np.searchsorted(xgrid, pts['dd long'])\n        iy = np.searchsorted(ygrid, pts['dd lat'])\n        bunch['cov_%s' % label] = coverages[:, -iy, ix].T\n\n    return bunch\n\n\ndef plot_species_distribution(species=(\"bradypus_variegatus_0\",\n                                       \"microryzomys_minutus_0\")):\n    \"\"\"\n    Plot the species distribution.\n    \"\"\"\n    if len(species) > 2:\n        print(\"Note: when more than two species are provided,\"\n              \" only the first two will be used\")\n\n    t0 = time()\n\n    # Load the compressed data\n    data = fetch_species_distributions()\n\n    # Set up the data grid\n    xgrid, ygrid = construct_grids(data)\n\n    # The grid in x,y coordinates\n    X, Y = np.meshgrid(xgrid, ygrid[::-1])\n\n    # create a bunch for each species\n    BV_bunch = create_species_bunch(species[0],\n                                    data.train, data.test,\n                                    data.coverages, xgrid, ygrid)\n    MM_bunch = create_species_bunch(species[1],\n                                    data.train, data.test,\n                                    data.coverages, xgrid, ygrid)\n\n    # background points (grid coordinates) for evaluation\n    np.random.seed(13)\n    background_points = np.c_[np.random.randint(low=0, high=data.Ny,\n                                                size=10000),\n                              np.random.randint(low=0, high=data.Nx,\n                                                size=10000)].T\n\n    # We'll make use of the fact that coverages[6] has measurements at all\n    # land points.  This will help us decide between land and water.\n    land_reference = data.coverages[6]\n\n    # Fit, predict, and plot for each species.\n    for i, species in enumerate([BV_bunch, MM_bunch]):\n        print(\"_\" * 80)\n        print(\"Modeling distribution of species '%s'\" % species.name)\n\n        # Standardize features\n        mean = species.cov_train.mean(axis=0)\n        std = species.cov_train.std(axis=0)\n        train_cover_std = (species.cov_train - mean) / std\n\n        # Fit OneClassSVM\n        print(\" - fit OneClassSVM ... \", end='')\n        clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.5)\n        clf.fit(train_cover_std)\n        print(\"done.\")\n\n        # Plot map of South America\n        plt.subplot(1, 2, i + 1)\n        if basemap:\n            print(\" - plot coastlines using basemap\")\n            m = Basemap(projection='cyl', llcrnrlat=Y.min(),\n                        urcrnrlat=Y.max(), llcrnrlon=X.min(),\n                        urcrnrlon=X.max(), resolution='c')\n            m.drawcoastlines()\n            m.drawcountries()\n        else:\n            print(\" - plot coastlines from coverage\")\n            plt.contour(X, Y, land_reference,\n                        levels=[-9998], colors=\"k\",\n                        linestyles=\"solid\")\n            plt.xticks([])\n            plt.yticks([])\n\n        print(\" - predict species distribution\")\n\n        # Predict species distribution using the training data\n        Z = np.ones((data.Ny, data.Nx), dtype=np.float64)\n\n        # We'll predict only for the land points.\n        idx = np.where(land_reference > -9999)\n        coverages_land = data.coverages[:, idx[0], idx[1]].T\n\n        pred = clf.decision_function((coverages_land - mean) / std)\n        Z *= pred.min()\n        Z[idx[0], idx[1]] = pred\n\n        levels = np.linspace(Z.min(), Z.max(), 25)\n        Z[land_reference == -9999] = -9999\n\n        # plot contours of the prediction\n        plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)\n        plt.colorbar(format='%.2f')\n\n        # scatter training/testing points\n        plt.scatter(species.pts_train['dd long'], species.pts_train['dd lat'],\n                    s=2 ** 2, c='black',\n                    marker='^', label='train')\n        plt.scatter(species.pts_test['dd long'], species.pts_test['dd lat'],\n                    s=2 ** 2, c='black',\n                    marker='x', label='test')\n        plt.legend()\n        plt.title(species.name)\n        plt.axis('equal')\n\n        # Compute AUC with regards to background points\n        pred_background = Z[background_points[0], background_points[1]]\n        pred_test = clf.decision_function((species.cov_test - mean) / std)\n        scores = np.r_[pred_test, pred_background]\n        y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]\n        fpr, tpr, thresholds = metrics.roc_curve(y, scores)\n        roc_auc = metrics.auc(fpr, tpr)\n        plt.text(-35, -70, \"AUC: %.3f\" % roc_auc, ha=\"right\")\n        print(\"\\n Area under the ROC curve : %f\" % roc_auc)\n\n    print(\"\\ntime elapsed: %.2fs\" % (time() - t0))\n\n\nplot_species_distribution()\nplt.show()"
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